first

docker/transcription-worker/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies for faster-whisper
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY main.py .
+
+EXPOSE 8000
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "1"]

docker/transcription-worker/main.py

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+"""
+Speech2Text MBIP — Transcription Worker
+FastAPI service wrapping faster-whisper for Bahasa Melayu transcription.
+Internal-only service; not exposed to internet.
+"""
+
+import os
+import tempfile
+import logging
+from pathlib import Path
+from typing import Optional
+
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from pydantic import BaseModel
+from faster_whisper import WhisperModel
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="Speech2Text Transcription Worker", docs_url=None, redoc_url=None)
+
+# Config from environment
+MODEL_SIZE = os.getenv("WHISPER_MODEL", "small")
+LANGUAGE = os.getenv("WHISPER_LANGUAGE", "ms")
+DEVICE = os.getenv("WHISPER_DEVICE", "cpu")
+COMPUTE_TYPE = os.getenv("WHISPER_COMPUTE_TYPE", "int8")
+
+logger.info(f"Loading Whisper model: {MODEL_SIZE}, device: {DEVICE}, compute: {COMPUTE_TYPE}")
+
+# Load model once at startup
+model = WhisperModel(MODEL_SIZE, device=DEVICE, compute_type=COMPUTE_TYPE)
+
+logger.info("Whisper model loaded successfully.")
+
+
+class TranscribeResponse(BaseModel):
+    success: bool
+    transcript: Optional[str] = None
+    language: Optional[str] = None
+    confidence: Optional[float] = None
+    duration_seconds: Optional[float] = None
+    error: Optional[str] = None
+
+
+@app.get("/health")
+def health_check():
+    return {"status": "ok", "model": MODEL_SIZE, "language": LANGUAGE}
+
+
+@app.post("/transcribe", response_model=TranscribeResponse)
+async def transcribe_audio(audio: UploadFile = File(...)):
+    """
+    Terima fail audio, transkripkan ke teks Bahasa Melayu.
+    Hanya accessible dari dalam Docker network sahaja.
+    """
+    # Validate content type
+    allowed_types = {
+        "audio/mpeg", "audio/mp3", "audio/wav", "audio/x-wav",
+        "audio/mp4", "audio/x-m4a", "audio/aac", "video/mp4",
+        "audio/ogg", "audio/flac", "audio/webm",
+    }
+
+    if audio.content_type and audio.content_type not in allowed_types:
+        logger.warning(f"Rejected content type: {audio.content_type}")
+        raise HTTPException(status_code=400, detail=f"Unsupported audio format: {audio.content_type}")
+
+    # Save to temp file
+    suffix = Path(audio.filename or "audio.mp3").suffix or ".mp3"
+
+    try:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+            content = await audio.read()
+            tmp.write(content)
+            tmp_path = tmp.name
+
+        logger.info(f"Transcribing: {audio.filename}, size: {len(content)} bytes")
+
+        # Run transcription
+        segments, info = model.transcribe(
+            tmp_path,
+            language=LANGUAGE,
+            beam_size=5,
+            vad_filter=True,
+        )
+
+        # Collect segments
+        all_text = []
+        total_confidence = []
+
+        for segment in segments:
+            all_text.append(segment.text.strip())
+            # faster-whisper segments have avg_logprob; convert to 0-1 range
+            if hasattr(segment, "avg_logprob") and segment.avg_logprob is not None:
+                import math
+                confidence = min(1.0, max(0.0, math.exp(segment.avg_logprob)))
+                total_confidence.append(confidence)
+
+        transcript = " ".join(all_text).strip()
+        avg_confidence = sum(total_confidence) / len(total_confidence) if total_confidence else None
+        duration = info.duration if hasattr(info, "duration") else None
+
+        logger.info(f"Transcription complete: {len(transcript)} chars, lang: {info.language}")
+
+        return TranscribeResponse(
+            success=True,
+            transcript=transcript,
+            language=info.language,
+            confidence=round(avg_confidence, 4) if avg_confidence else None,
+            duration_seconds=round(duration, 2) if duration else None,
+        )
+
+    except Exception as e:
+        logger.error(f"Transcription failed: {e}", exc_info=True)
+        return TranscribeResponse(
+            success=False,
+            error=str(e),
+        )
+
+    finally:
+        # Always cleanup temp file
+        if "tmp_path" in locals():
+            try:
+                os.unlink(tmp_path)
+            except Exception:
+                pass

docker/transcription-worker/requirements.txt

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+fastapi==0.115.0
+uvicorn==0.30.6
+faster-whisper==1.0.3
+python-multipart==0.0.9
+pydantic==2.8.2
+requests==2.32.3